With the advent of high-throughput molecular assay technologies, biologists are having to deal with the[unreadable] analysis of high-dimensional genomic datasets. While statistical methods have been proposed for issues[unreadable] such as differential expression with these data, relatively little work has been done in terms of[unreadable] incorporating biological knowledge in the statistical analysis of high-throughput biological data in[unreadable] human disease settings.[unreadable] In this grant, we propose the development of statistical procedures for modeling of complex highdimensional[unreadable] biological data with an emphasis towards incorporating functional biological knowledge.[unreadable] The methods we propose will be implemented and distributed in software available to biologists. While the[unreadable] major biological data example in this grant is from a microarray experiment in cancer, the methods[unreadable] proposed here are general and can be developed for studying high-dimensional genotype-phenotype[unreadable] associations in other contexts. Given this, we propose the following aims:[unreadable] 1. Development of hierarchical models for modelling of high-dimensional data in complex cell systems.[unreadable] 2. Development of statistical methodology for the identification of disease progressor genes.[unreadable] 3. Development of statistical methodology for assessing the role of functional pathways based on[unreadable] integration of gene expression and pathway data.[unreadable] 4. Development of statistical methodology for determining regions of overexpression and underexpression[unreadable] based on integration of gene expression and chromosomal location data.[unreadable] 5. Dissemination of these results in user-friendly statistical software.